Improved Algorithm for Wavelet Threshold Denoising and Application in Crack Image Recognition

Abstract:

In crack image recognition, Donoho’s universal wavelet threshold de-noising method appears "over-kill" phenomenon due to the lack of self-adaptability of threshold value; hence the image may lose its edge details. To handle this problem, the Donoho’s universal threshold and threshold function are improved and an adaptive determination method of threshold coefficient is introduced in this paper. Experimental results shows that the proposed method can effectively remove digital image noise and achieve a better edge protection, higher edge preservation index, better visual effects and higher peak signal-to-noise ratio.

• Periodical: Advanced Materials Research (Volumes 945-949)
• Pages: 1846-1850
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.945-949.1846
• Online since: June 2014
• Authors: Hai Biao Li*, Xin Xia
• Keywords: Crack Recognition, Image De-Noising, Threshold Method, Wavelet Transform (WT)

References

[1] Coyle E J, Gabbouj M , Lin J H. From Median Filters to Optimal Stack Filtering, IEEE Internat. Symp. Circuits Systems, vol. 1, pp.9-12, (1991).

DOI: 10.1109/iscas.1991.176260

[2] Donoho D L. De-noising by soft-thresholding, IEEE Transactions on Information Theory, vol. 41 no. 3: pp.613-627, (1995).

DOI: 10.1109/18.382009

[3] Bolster, E.J. et al. Fast, Feature-Based Wavelet Shrinkage Algorithm for Image Denoising, Proceedings of the KIMAS, pp.722-728, (2003).

[4] YANG Hui-Xian, WANG Xu-SiACTA. AUTOMATICA SINICA, vol. 37, no. 10, pp.1167-1174, (2011).

[5] L. Sanders and I. W. Selesnick, Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency, IEEE Transactions on Signal Processing, Vol. 50, No. 11, pp.2744-2756, (2002).

DOI: 10.1109/tsp.2002.804091

[6] T. T. Cai and B. W. Silverman: The Indian Journal of Statistics, Vol. 63, Series B, Pt. 2, pp.127-148, (2001).

[7] G. Y. Chen and T. D. Bui, Multiwavelet Denoising using Neighbouring Coefficients, IEEE Signal Processing Letters, vol. 10, no. 7, pp.211-214, (2003).

DOI: 10.1109/lsp.2003.811586

[8] W. Shengqian, Z. Yuanhua and Z. Daowen, Adaptive shrinkage denoising using neighbourhood characteristic, Electronics Letters, vol. 38, no. 11, pp.502-503, (2002).

DOI: 10.1049/el:20020352

[9] L. Sendur and I. W. Selesnick: IEEE Transactions on Signal Processing, vol. 50, no. 11, pp.2744-2756, (2002).

DOI: 10.1109/tsp.2002.804091

[10] L. Sendur and I. W. Selesnick, Bivariate Shrinkage with Local Variance Estimation, IEEE Signal Processing Letters, Vol. 9, No. 12, pp.438-441, (2002).

DOI: 10.1109/lsp.2002.806054